Gemini 3 is the only model I've found that can reason spatially. The results here are accurate to my experiments with putting LLM NPCs in simulated worlds. I was surprised that most VLLMs cannot reliably tell if a character is facing left or right, they will confidently lie no matter what you do (even gemini 3 cannot do it reliably). I guess it's just not in the training data. That said Qwen3VL models are smaller/fas…
Show HN: Only 1 LLM can fly a drone
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Re: Show HN: Only 1 LLM can fly a drone
#82Gemini 3 is the only model I've found that can reason spatially. The results here are accurate to my experiments with putting LLM NPCs in simulated worlds. I was surprised that most VLLMs cannot reliably tell if a character is facing left or right, they will confidently lie no matter what you do (even gemini 3 cannot do it reliably). I guess it's just not in the training data. That said Qwen3VL models are smaller/fas…
I suspect the latency on Gemini 3 makes it non-viable for a real-time control loop though. Even if the reasoning works, the input token costs would destroy the unit economics pretty quickly. I'd be worried about relying on that kind of API overhead for the critical path.
They say this is going to happen to every task after the stop subsidizing token costs.
Re: Show HN: Only 1 LLM can fly a drone
#83Earlier quoted context omitted.
You see to be talking past him and ignoring what they are actually saying. LLMs are a higher level construct than PID loops. With things like autopilot I can give the controller a command like 'Go from A to B', and chain constructs like this to accomplish a task. With an LLM I can give the drone/LLM system complex command that I'd never be able to encode to a controller alone. "Fly a grid over my neighborhood, docume…
They are text generators, and yes they are pretty good, but that really is all they are, they don't actually learn, they don't actually think. Every "intelligence" feature by every major AI company relies on semantic trickery and managing context windows. It even says it right on the tin; Large LANGUAGE Model. Let me put it this way: What OP built is an airplane in which a pilot doesn't have a control stick, but they…
Give the LLM agent write acces to a text file to take notes and it can actually learn. Not really realiable, but some seem to get useful results. They ain't just text generators anymore.
(but I agree that it does not seem the smartest way to control a plane with a keyboard)
Re: Show HN: Only 1 LLM can fly a drone
#84Re: Show HN: Only 1 LLM can fly a drone
#85Earlier quoted context omitted.
I mean semantically language evolved as an interpretation for the material world, so assuming that you can describe a problem in language, and considering that there exists a solution to said problem that is describable in language, then I'm sure a big enough LLM could do it... but you can also calculate highly detailed orbital maps with epicycles if you just keep adding more... you just don't because it's a waste of…
note that the control systems you were talking about before (i.e. PID) would probably take hold pretty directly in a tiny network, and exactly because of that limitation, be far less likely to contain 'hallucinations'. object avoidance and path planning are likely similar. since this is a limited and continuous domain, its a far better one for neural training than natural language. I guess this notion that a language…
Re: Show HN: Only 1 LLM can fly a drone
#86Earlier quoted context omitted.
I suspect the latency on Gemini 3 makes it non-viable for a real-time control loop though. Even if the reasoning works, the input token costs would destroy the unit economics pretty quickly. I'd be worried about relying on that kind of API overhead for the critical path.
> the input token costs would destroy the unit economics pretty quickly. They say this is going to happen to every task after the stop subsidizing token costs.
Re: Show HN: Only 1 LLM can fly a drone
#87Re: Show HN: Only 1 LLM can fly a drone
#88Earlier quoted context omitted.
I don't think you understand what an "LLM" is. They're text generators. We've had autopilot since the 1930s that relies on measurable things... like PID loops, direct sensor input. You don't need the "language model" part to run an autopilot, that's just silly.
LLMs can do chat-completion, they don't do only chat completion. There are LLMs for image generation, voice generation, video generation and possibly more. The camera of a drone inputs images for the LLM, then it determines what action take based on that. Similar to if you asked ChatGPT "there is a tree in this picture, if you were operating a drone, what action would you take to avoid collision", except the "there i…
That part isn’t handled by an LLM
> voice generation,
That part isn’t handled by an LLM
> video generation
That part isn’t handled by an LLM
Re: Show HN: Only 1 LLM can fly a drone
#89Earlier quoted context omitted.
No science detected. Without comparison to some null hypothesis (a random policy), this article is hogwash.
Given that all the other agents failed to find any creatures, it's hard to imagine that a random policy would except by extreme coincidence.
For some problems, randomness outperforms incompetent reasoning
Re: Show HN: Only 1 LLM can fly a drone
#90Earlier quoted context omitted.
> the input token costs would destroy the unit economics pretty quickly. They say this is going to happen to every task after the stop subsidizing token costs.
Not for coding though - I'd buy 4 H200's and stick them in my basement if i had to